The prices of many commodities, such as oil or electricity, vary through time. When the commodity is an input to a manufacturing process, the commodity's price may represent a large portion of the manufacturer's cost of running the process. Consequently, the time-varying price of the commodity may represent a large portion of the variability in manufacturing costs and thus a large portion of the variability in the manufacturer's profits.
Both to hold down total manufacturing costs and to decrease variability in those costs, manufacturers attempt to purchase price-varying commodities when their price is low. In some cases, a manufacturer buys a large quantity of the commodity at a low price and stockpiles it for later use. In other cases, the manufacturer has less flexibility because some commodities, such as electricity, are not readily stockpiled. In these cases, the manufacturer must purchase the commodity, whatever its current price, at the time the commodity is used as an input to the manufacturing process. Where stockpiling is not an option, the manufacturer may still decrease costs and variability by scheduling the process to run only when the price of the commodity is expected to be low. Of course, this is only possible if the manufacturer has some extra manufacturing capacity and thus need not run the process all the time.
Manufacturers use various known methods for predicting when the price of a time-varying commodity will be low. For some commodities, price variations are tied to predictable variations in supply and demand as when, for example, home heating oil prices rise during the winter in colder climates. For other commodities, or at timescales finer than the four seasons of the year, prices can change wildly and less predictably. This latter situation characterizes, for example, the short-term, hour by hour, market for electricity bought by large industrial users. Experience with existing methods has shown that making accurate predictions of the short-term price of electricity over the next few days is extraordinarily difficult. This is due in part to the short time frame of the prediction: too much can happen too quickly to allow long term, seasonal effects to smooth out short term variations. However, manufacturers are tied to such a short time frame if the process uses commodities that are not readily stockpiled. The manufacturer may be able to postpone running the process for an hour or even for a day, but rare is the manufacturer that can postpone a process in anticipation of lower energy prices three months from now.
Another factor leading to inaccuracies in price prediction is the large number of variables that can affect the short term price of a commodity. Many of these variables are unknowable to the predictor. Again using electricity as an example, another manufacturer may unexpectedly encounter difficulties and shut down a power-intensive manufacturing line. That shutdown lowers the demand for electricity which may very quickly result in a lower price as the electric power provider attempts to even out demand by varying the price. As another example, a power supply station may go offline, decreasing supply and increasing price.
What is needed is a way to schedule when to run a process to take advantage of time-varying input prices while avoiding the inaccuracies associated with predicting future prices of the input.